Computes and compares multiple measures of separation and overlap between phonological categories (for example vowels or consonants) in arbitrary multi-dimensional acoustic spaces such as formant values, mel-frequency cepstral coefficients (MFCCs), duration, or learned embeddings. The main entry point, phontrast(), reports several contrast metrics in one call -- Jensen-Shannon divergence and distance (Lin, 1991) <doi:10.1109/18.61115>, the Pillai-Bartlett trace, Bhattacharyya distance and affinity, Mahalanobis distance, and proportional overlap -- globally or by group on a common separation-oriented scale, with bootstrap confidence intervals. Also provides utilities for preparing estimates for downstream modelling such as generalized additive models and mixed-effects models. Formerly released as phonJSD'.

This provides utilities for creating classed error and warning conditions based on where the error originated.

Support functions, data sets, and vignettes for the psych package. Contains several of the biggest data sets for the psych package as well as four vignettes. A few helper functions for file manipulation are included as well. For more information, see the <https://personality-project.org/r/> web page.

This package provides the infrastructure to define and analyze solutions to Partially Observable Markov Decision Process (POMDP) models. Interfaces to various exact and approximate solution algorithms are available, including value iteration, point-based value iteration, and SARSOP. Hahsler and Cassandra <doi:10.32614/RJ-2024-021>.

For a multivariate dataset with independent Poisson measurement error, calculates principal components of transformed latent Poisson means. T. Kenney, T. Huang, H. Gu (2019) <arXiv:1904.11745>.

Monte Carlo based model choice for applied phylogenetics of continuous traits. Method described in Carl Boettiger, Graham Coop, Peter Ralph (2012) Is your phylogeny informative? Measuring the power of comparative methods, Evolution 66 (7) 2240-51. <doi:10.1111/j.1558-5646.2011.01574.x>.

This package implements the Proximal Hamiltonian Monte Carlo (p-HMC) algorithm for Bayesian sampling and estimation from non-differentiable target densities. The method decomposes a target potential into a smooth component f(x) and a non-smooth convex component g(x), approximating only g(x) via its Moreau-Yosida envelope while retaining exact gradient information for f(x). This approach, based on the methodology described in Shukla, Vats, and Chi (2025) <doi:10.48550/arXiv.2510.22252>, yields improved Hamiltonian conservation over full-potential smoothing approaches. The package provides generalized routines accepting user-defined probability density functions, log-likelihoods, priors, and proximal operators, together with automated hyperparameter tuning for the Moreau-Yosida regularization parameter, Markov chain Monte Carlo convergence diagnostics, effective sample size computation, and model evaluation metrics including the Akaike information criterion and Bayesian information criterion.

Generate port files for foreign function interfaces to C libraries by parsing C-family header files with CastXML'.

This package provides a semi-parametric estimation method for the Cox model with left-truncated data using augmented information from the marginal of truncation times.

Enrichment analysis enables researchers to uncover mechanisms underlying a phenotype. However, conventional methods for enrichment analysis do not take into account protein-protein interaction information, resulting in incomplete conclusions. pathfindR is a tool for enrichment analysis utilizing active subnetworks. The main function identifies active subnetworks in a protein-protein interaction network using a user-provided list of genes and associated p values. It then performs enrichment analyses on the identified subnetworks, identifying enriched terms (i.e. pathways or, more broadly, gene sets) that possibly underlie the phenotype of interest. pathfindR also offers functionalities to cluster the enriched terms and identify representative terms in each cluster, to score the enriched terms per sample and to visualize analysis results. The enrichment, clustering and other methods implemented in pathfindR are described in detail in Ulgen E, Ozisik O, Sezerman OU. 2019. pathfindR': An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks. Front. Genet. <doi:10.3389/fgene.2019.00858>.

Identifies potential target sequences for a given set of primers and generates phylogenetic trees annotated with the taxonomies of the predicted amplification products.

Perform a differential analysis at pathway level based on metabolite quantifications and information on pathway metabolite composition. The method, described in Guilmineau et al (2025) <doi:10.1186/s12859-025-06118-z> is based on a Principal Component Analysis step and on a linear mixed model. Automatic query of metabolic pathways is also implemented.

Single arm phase 2 oncology trial. For more details see P. Gao (2024) <doi:10.1080/10543406.2024.2341673>.

This package provides a collection of scripts and data files for the statistics text: "Process Improvement using Data" <https://learnche.org/pid/> and the online course "Experimentation for Improvement" found on Coursera. The package contains code for designed experiments, data sets and other convenience functions used in the book.

Enables computation of epidemiological statistics, including those where counts or mortality rates of the reference population are used. Currently supported: excess hazard models (Dickman, Sloggett, Hills, and Hakulinen (2012) <doi:10.1002/sim.1597>), rates, mean survival times, relative/net survival (in particular the Ederer II (Ederer and Heise (1959)) and Pohar Perme (Pohar Perme, Stare, and Esteve (2012) <doi:10.1111/j.1541-0420.2011.01640.x>) estimators), and standardized incidence and mortality ratios, all of which can be easily adjusted for by covariates such as age. Fast splitting and aggregation of Lexis objects (from package Epi') and other computations achieved using data.table'.

This package provides functions to setup a personal R package that attaches given libraries and exports personal helper functions.

Providing functions to diagnose and make inferences from various linear models, such as those obtained from aov', lm', glm', gls', lme', lmer', glmmTMB and semireg'. Inferences include predicted means and standard errors, contrasts, multiple comparisons, permutation tests, adjusted R-square and graphs.

Improving graphics by ameliorating order effects, using Eulerian tours and Hamiltonian decompositions of graphs. References for the methods presented here are C.B. Hurley and R.W. Oldford (2010) <doi:10.1198/jcgs.2010.09136> and C.B. Hurley and R.W. Oldford (2011) <doi:10.1007/s00180-011-0229-5>.

Routines for two different test types, the Constant Conditional Correlation (CCC) test and the Vectorial Independence (VI) test are provided (Kurz and Spanhel (2022) <doi:10.1214/22-EJS2051>). The tests can be applied to check whether a conditional copula coincides with its partial copula. Functions to test whether a regular vine copula satisfies the so-called simplifying assumption or to test a single copula within a regular vine copula to be a (j-1)-th order partial copula are available. The CCC test comes with a decision tree approach to allow testing in high-dimensional settings.

This package provides a set of functions designed to calculate the standardised precipitation and standardised precipitation evapotranspiration indices using NASA POWER data as described in Blain et al. (2023) <doi:10.2139/ssrn.4442843>. These indices are calculated using a reference data source. The functions verify if the indices estimates meet the assumption of normality and how well NASA POWER estimates represent real-world data. Indices are calculated in a routine mode. Potential evapotranspiration amounts and the difference between rainfall and potential evapotranspiration are also calculated. The functions adopt a basic time scale that splits each month into four periods. Days 1 to 7, days 8 to 14, days 15 to 21, and days 22 to 28, 29, 30, or 31, where TS=4 corresponds to a 1-month length moving window (calculated 4 times per month) and TS=48 corresponds to a 12-month length moving window (calculated 4 times per month).

Programmatic interface to the PhenoCam web services (<https://phenocam.nau.edu/webcam>). Allows for easy downloading of PhenoCam data directly to your R workspace or your computer and provides post-processing routines for consistent and easy timeseries outlier detection, smoothing and estimation of phenological transition dates. Methods for this package are described in detail in Hufkens et. al (2018) <doi:10.1111/2041-210X.12970>.

Executes simple parametric models for right-censored survival data. Functionality emulates capabilities in Minitab', including fitting right-censored data, assessing fit, plotting survival functions, and summary statistics and probabilities.

Includes functions to wrap most endpoints of the PaleobioDB API and to visualize and process the obtained fossil data. The API documentation for the Paleobiology Database can be found at <https://paleobiodb.org/data1.2/>.

Applies cartographic projections to spatial data frames containing geographic coordinates. Projection methods are based on the D3.js ecosystem <doi:10.1109/TVCG.2011.185> and use spherical geometry rather than ellipsoidal geodesic models.